Prof. Dr. Matthias Schmid
Institute of medical Biometry, Computer Science and Epidemiology
sekretariat@imbie.uni-bonn.de View member: Prof. Dr. Matthias Schmid
Kidney international reports
INTRODUCTION: The rate of decline in estimated glomerular filtration rate (GFR, eGFR) is increasingly recognized as a quantitative marker of chronic kidney disease (CKD) progression. However, data on eGFR slopes have mainly been reported in cohorts enriched for fast progression and the heterogeneity of eGFR slopes across the spectrum of CKD remains poorly defined.
METHODS: In 5214 participants of the German CKD (GCKD) study, we modeled eGFR slopes using per-protocol and clinical measurements. We used linear-mixed effects models, with eGFR slope as the outcome and baseline demographics as independent variables to (i) describe eGFR slope heterogeneity; (ii) assess differences by CKD etiology, eGFR and urinary albumin-to-creatinine ratio (UACR) categories, sex, and age; and (iii) determine associations of slopes with estimated eGFR decline (30%, 40%, and 57%) and observed end points (kidney failure with replacement therapy, mortality).
RESULTS: On average, 9 eGFR values per participant (interquartile range: 7-12) over 6.5 years were used for slope calculation. The adjusted mean annual eGFR slope was -1.43 ml/min per 1.73 m. Slopes were similar across eGFR categories, but steeper with higher UACR. Faster eGFR decline was observed in participants of younger age and in those with polycystic kidney disease or diabetic kidney disease (DKD). Although eGFR slopes did not consistently differ by sex, women with diabetes as the leading cause of CKD had lower slopes than their male counterparts. A rapid annual decline (> 5 ml/min per 1.73 m) occurred in 4.3%, with variation in frequency by CKD cause and UACR.
CONCLUSION: In conclusion, though the average eGFR slope was low, it varied considerably, depending on CKD etiology and UACR. This data may help to put slope estimates in individual patients and defined subpopulations into perspective.
© 2025 International Society of Nephrology. Published by Elsevier Inc.
PMID: 41542113
Institute of medical Biometry, Computer Science and Epidemiology
sekretariat@imbie.uni-bonn.de View member: Prof. Dr. Matthias Schmid